Elsevier

NeuroImage

Volume 50, Issue 4, 1 May 2010, Pages 1690-1701
NeuroImage

Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity

https://doi.org/10.1016/j.neuroimage.2010.01.002Get rights and content

Abstract

The resting brain exhibits coherent patterns of spontaneous low-frequency BOLD fluctuations. These so-called resting-state functional connectivity (RSFC) networks are posited to reflect intrinsic representations of functional systems commonly implicated in cognitive function. Yet, the direct relationship between RSFC and the BOLD response induced by task performance remains unclear. Here we examine the relationship between a region's pattern of RSFC across participants and that same region's level of BOLD activation during an Eriksen Flanker task. To achieve this goal we employed a voxel-matched regression method, which assessed whether the magnitude of task-induced activity at each brain voxel could be predicted by measures of RSFC strength for the same voxel, across 26 healthy adults. We examined relationships between task-induced activation and RSFC strength for six different seed regions [Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E., 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U. S. A. 102, 9673–9678.], as well as the “default mode” and “task-positive” resting-state networks in their entirety. Our results indicate that, for a number of brain regions, inter-individual differences in task-induced BOLD activity were predicted by one of two resting-state properties: (1) the region's positive connectivity strength with the task-positive network, or (2) its negative connectivity with the default mode network. Strikingly, most of the regions exhibiting a significant relationship between their RSFC properties and task-induced BOLD activity were located in transition zones between the default mode and task-positive networks. These results suggest that a common mechanism governs many brain regions' neural activity during rest and its neural activity during task performance.

Introduction

Central to adaptive brain function is the ability to recruit appropriate brain regions for the task at hand. Task-based neuroimaging research has led to substantial advances in our understanding of the regions involved in the performance of a given task. Nevertheless, the extent to which functional systems related to task performance are predefined remains poorly understood. Resting-state functional connectivity (RSFC) studies suggest that the functional systems observed during task performance are intrinsically represented in the brain by coherent low-frequency (< 0.1 Hz) fluctuations in the BOLD signal within distinct functional networks (Biswal et al., 1995, Damoiseaux et al., 2006, Fox and Raichle, 2007, Fox et al., 2005, Greicius et al., 2003, Kiviniemi et al., 2009, Margulies et al., 2007, Vincent et al., 2007). Numerous studies have drawn attention to similarities between patterns of task-based activation and functional networks detected during rest (Greicius and Menon, 2004, Smith et al., 2009, Thomason et al., 2008, Toro et al., 2008). However, no study has directly linked resting state phenomena and task-related BOLD activity in the same subjects, and little is known about the relevance of intrinsic RSFC for task-related neural activity.

Here we examine the extent to which inter-individual differences in a region's task-induced BOLD response can be predicted by inter-individual differences in that region's RSFC characteristics. To tackle this issue we took advantage of the availability of a dataset that included both resting state and Eriksen Flanker task scans in the same set of 26 participants (Kelly et al., 2008). The Eriksen Flanker task is commonly used to probe attention and has well-characterized activation patterns (Brown, 2009, Bunge et al., 2002, Ochsner et al., 2009). As such, we aimed to identify one or more well-characterized, easily reproducible, and highly reliable functional networks that are thought to overlap with patterns of activation or deactivation during performance of attentional paradigms such as the Flanker task. Accordingly, we selected two of the most commonly studied large-scale networks in the resting state literature, namely the “task positive” and “default mode” networks (Fox et al., 2005). Specifically, across 26 participants, we relate the magnitude of each voxel's BOLD responses evoked during Eriksen Flanker task performance to the same voxel's RSFC with the default mode and task-positive networks as previously defined by Fox et al. (2005). Regions within the default mode or “task-negative” network typically show a negative BOLD response (or deactivation) during goal-directed tasks and include areas such as ventromedial prefrontal, posterior cingulate, lateral parietal, medial temporal cortex and precuneus (Dosenbach et al., 2007, Fox et al., 2005, Toro et al., 2008). In contrast, the task-positive network comprises regions that are typically activated during performance of stimulus- and goal-directed cognitive tasks. Areas in this network include the frontal eye fields, mid-cingulate cortex, intraparietal sulcus and inferior parietal cortex (Dosenbach et al., 2007, Fox et al., 2005, Toro et al., 2008). Given the implication of the default mode and task-positive networks in both cognitive performance and the resting-state, these networks provide an ideal starting ground to explore the potential of resting-state fMRI measures for explaining BOLD activity induced by task performance.

Since default mode regions typically deactivate during task performance, we predicted that greater RSFC strength with default mode network regions would be associated with lower task-induced BOLD response magnitudes. In contrast, task-positive regions are typically activated during task performance, warranting the prediction that greater RSFC strength with task-positive network regions would be associated with higher task-induced BOLD response magnitudes. To test our predictions, we used a variant of the general linear model approach typically employed for analyses in functional neuroimaging. In the standard approach, effects of interest across all voxels in the brain are detected using the same model for each voxel. In contrast, we employed a unique linear regression model for each voxel, an approach we refer to as voxel-matched regression. Specifically, for each participant, we first calculated RSFC between each voxel and each of the six seed regions previously used to define the default mode and task-positive networks (Fox et al., 2005). Next, for the same participants, we calculated each voxel's BOLD response during performance of the Eriksen Flanker task. Finally, using a unique linear regression model for each voxel, we assessed the relationship between a voxel's RSFC strength for each of the six seed regions, and the magnitude of that same voxel's task-induced BOLD response across participants. While the present work is exploratory in nature, it is worth noting that areas characterized by greater variability in RSFC strength across participants have a greater likelihood of exhibiting meaningful variation (with respect to RSFC/task relationships), compared to areas that are relatively invariant across participants. In this regard, we draw attention to the boundary areas (i.e., edges) of the RSFC networks. They are sharply defined for any given participant (Cohen et al., 2008), but the specific locations of the boundaries are most likely to vary across participants.

Beyond the individual component regions of the default mode and task-positive networks, we assessed the relationship between a voxel's task-induced BOLD response and its connectivity with each of the two networks in their entirety. This allowed for a more general, integrated assessment of the relationship between task-induced activity and the two resting-state networks in their entirety, as compared to assessing that relationship at the level of singular parts (small seed ROIs) of these networks. We predicted that the patterns of RSFC/task-induced activity relationships would be similar to those observed for the individual seed regions within the networks. Finally, to assure that our findings were not related to methodological factors of the seed-based approach for measuring RSFC or driven by the a priori selection of seed ROIs, we performed Independent Component Analysis (ICA) to identify RSFC with a default mode component and similarly used this RSFC measure to predict the BOLD activity induced by the Eriksen Flanker task.

Section snippets

Participants and experimental paradigm

The present work used a dataset previously published by our laboratory (Kelly et al., 2008). Twenty-six right-handed adults (age: 28.1 ± 8.5 years) participated in this event-related fMRI study. Participants provided signed informed consent in accordance with the institutional review boards of NYU and the NYU School of Medicine and were financially compensated for their participation. Two 5-min functional scans were administered while participants completed a slow event-related Eriksen Flanker

RSFC strength predicts task-induced activity

At each voxel in the brain, regression analyses assessed the relationship between task-induced BOLD responses and RSFC with each of the six seed ROIs (see Supplementary Figure 4 for results with a seed ROI located in ACC). For each seed, RSFC significantly predicted overall task-induced brain activity (Congruent + Incongruent > Baseline) in a number of brain regions (see Fig. 2). The direction of the relationship, i.e., positive or negative, depended on the specific network membership of each

Discussion

In the present work we examined inter-individual differences in the magnitude of a region's task-induced activity in relation to inter-individual differences in the same region's resting-state functional connectivity (RSFC) properties. For a number of regions, higher task-induced increases in neural activity during a standard selective attention task were significantly related to stronger positive connectivity with the task-positive network. Higher task-induced increases in neural activity were

Summary

Increased interest in resting-state fMRI has focused attention on the intrinsic properties of the brain. Here we were able to link functional connectivity measures obtained during rest to fMRI measures obtained during task performance. Using voxel-matched regression analyses, we showed that across participants a region's intrinsic activity as measured during rest predicted that same region's activity induced by a Flanker task. Building on two well-characterized RSFC networks, this work

Acknowledgments

The authors thank all participants for their cooperation and Amy Roy, Maki Koyama, and Jonathan Adelstein for helpful comments on earlier versions of this manuscript. This research was partially supported by grants from NIMH (R01MH083246), Autism Speaks, the Stavros Niarchos Foundation, the Leon Levy Foundation, and gifts from Joseph P. Healy, Linda and Richard Schaps, Jill and Bob Smith, and the endowment provided by Phyllis Green and Randolph Cōwen.

References (65)

  • EmeryL. et al.

    Age-related changes in neural activity during performance matched working memory manipulation

    NeuroImage

    (2008)
  • FoxM.D. et al.

    Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior

    Neuron

    (2007)
  • FranssonP.

    How default is the default mode of brain function? Further evidence from intrinsic BOLD signal fluctuations

    Neuropsychologia

    (2006)
  • HaaseL. et al.

    Cortical activation in response to pure taste stimuli during the physiological states of hunger and satiety

    NeuroImage

    (2009)
  • JenkinsonM. et al.

    A global optimisation method for robust affine registration of brain images

    Med. Image Anal.

    (2001)
  • JenkinsonM. et al.

    Improved optimisation for the robust and accurate linear registration and motion correction of brain images

    NeuroImage

    (2002)
  • KellyA.M. et al.

    Competition between functional brain networks mediates behavioral variability

    NeuroImage

    (2008)
  • KiviniemiV. et al.

    Independent component analysis of nondeterministic fMRI signal sources

    NeuroImage

    (2003)
  • MarguliesD.S. et al.

    Mapping the functional connectivity of anterior cingulate cortex

    NeuroImage

    (2007)
  • MostofskyS.H. et al.

    Atypical motor and sensory cortex activation in attention-deficit/hyperactivity disorder: a functional magnetic resonance imaging study of simple sequential finger tapping

    Biol. Psychiatry

    (2006)
  • SchultzJ. et al.

    BOLD signal in intraparietal sulcus covaries with magnitude of implicitly driven attention shifts

    NeuroImage

    (2009)
  • SiepN. et al.

    Hunger is the best spice: an fMRI study of the effects of attention, hunger and calorie content on food reward processing in the amygdala and orbitofrontal cortex

    Behav. Brain Res.

    (2009)
  • SkudlarskiP. et al.

    Measuring brain connectivity: diffusion tensor imaging validates resting state temporal correlations

    NeuroImage

    (2008)
  • Steyn-RossM.L. et al.

    Modeling brain activation patterns for the default and cognitive states

    NeuroImage

    (2009)
  • ThomasonM.E. et al.

    Default-mode function and task-induced deactivation have overlapping brain substrates in children

    NeuroImage

    (2008)
  • UrryH.L. et al.

    Individual differences in some (but not all) medial prefrontal regions reflect cognitive demand while regulating unpleasant emotion

    NeuroImage

    (2009)
  • Van EssenD.C.

    A population-average, landmark- and surface-based (PALS) atlas of human cerebral cortex

    NeuroImage

    (2005)
  • XiongJ. et al.

    Long-term motor training induced changes in regional cerebral blood flow in both task and resting states

    NeuroImage

    (2009)
  • ZuoX.N. et al.

    Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach.

    Neuroimage

    (2010)
  • Andersson, J.L.R., Jenkinson, M., Smith, S.M., 2007. TR07JA2 : Non-linear registration, aka Spatial normalisation....
  • BarnesA. et al.

    Endogenous human brain dynamics recover slowly following cognitive effort

    PLoS One

    (2009)
  • BeckmannC.F. et al.

    Investigations into resting-state connectivity using independent component analysis

    Philos. Trans. R. Soc. Lond. B Biol. Sci.

    (2005)
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